As computing technology has advanced, the types of different devices that can be used to watch audio/video (A/V) content has expanded, as has the number of video content sources. While users used to be limited to watching A/V content on their televisions received via broadcast radio waves, users are now able to watch A/V content on their computers, phones, televisions, etc., where that content can be received via the Internet, cellular data networks, cable television systems, and so forth. Given such wide variety of A/V content sources, each content provider typically desires to keep end users engaged with the content provider's own A/V content rather than A/V content from another content provider. However, it remains problematic for each content provider to know how to keep users engaged with their A/V content.
One technique that can be used to recommend A/V content to users to keep them engaged with a content provider is a collaborative recommendation technique that attempts to predict the utility of A/V content for a particular user based on the A/V content previously rated by other users. However, situations oftentimes arise in which few users switch from watching A/V content on one channel to watching A/V content on another channel. Such collaborative recommendation techniques are problematic because these collaborative recommendation techniques tend to give poor recommendations in situations in which very few users switch from watching A/V content on one channel to watching A/V content on another channel.